According to the recent McKinsey report on the economic potential of generative AI, this technology could create an additional value potential of $2.6 trillion to $4.4 trillion annually above what could be unlocked by other AI and analytics. Yet, a lot of companies approach generative AI adoption with a layer of reserve. Let’s see how to prepare for such a project.
The benefits are clear. It is true that generative AI has the potential to solve various business problems, boost efficiency, ignite creativity, and increase revenue. With all the possibilities it brings, Gen AI adoption seems to be the holy grail for many organizations.
However, generative AI implementation is not just about setting up a ChatGPT account. Getting from idea to full project rollout may be a complex undertaking that will take a long time. Because of that, it requires a strategic approach and proper preparation.
In this article, you’ll find the first patch of 39 questions* to help you plan a successful generative AI implementation. This time, we will focus on the business side of the project: identifying the right use cases, aligning the Gen AI implementation with your strategic goals, and evaluating its business impact.
* Based on the original list published on LinkedIn.
Generative AI vs. strategic goals
First, let’s see how generative AI implementation can support your strategic goals and KPIs.
#1 What are the current strategic goals of your organization?
By aligning the deployment of generative AI with your strategic goals, you ensure that the technology directly supports and advances those goals. This alignment helps allocate resources efficiently, gain a competitive advantage, mitigate risks, and ensure compatibility with your long-term vision. It enables you to prioritize areas with the highest potential impact, leverage Gen AI for innovation and productivity, and address ethical and security concerns. By incorporating generative AI in line with your strategic goals, you can maximize its value and ensure responsible and sustainable deployment, ultimately driving your organization’s success.
#2 How are those goals measured?
By taking the metrics into account from the very beginning, you ensure that the implementation of generative AI aligns with the evaluation criteria for strategic goals. This promotes accountability, transparency, and effective resource allocation, while also enabling continuous improvement and adaptability to changing circumstances.
Understanding and measuring strategic goals allows you to track progress, demonstrate the value of Gen AI, refine implementations, allocate resources strategically, and adapt goals as needed, ultimately enhancing the success and impact of AI-driven initiatives within your organization.
#3 How does your work contribute to the strategy, and what KPIs does it affect?
By considering how your work contributes to the strategy, you can implement generative AI in the areas that have the highest impact on your organization’s strategic goals. This alignment ensures that Gen AI is implemented in a way that directly supports the strategy, maximizes its value, and enhances the overall success of your organization.
Generative AI & internal processes
Now that you know how generative AI implementation can align with your strategic goals and KPIs, let’s think about the specific areas where it can support your business.
#4 What processes are delivering the most value?
Generative AI implementation often requires investments in data collection, infrastructure, and talent. When you target processes that deliver the most value, you can:
- prioritize Gen AI implementation in areas that will have the greatest impact and allocate resources strategically,
- ensure that your investments generate the highest return on investment,
- align with the organization’s goals.
#5 Would the company benefit from executing more of those processes?
By improving work efficiency, generative AI can help you increase the amount of work that needs to be accomplished in certain areas. Now, the question is which of the processes that are delivering the most value can actually benefit from such an amplification without compromising on quality. In other words: the processes that you’re looking for should be both impactful and scalable.
#6 What processes are underperforming?
Or, maybe, you’ll see more value in improving something that doesn’t work that well and needs to be fixed rather than scaling what already works? When considering the areas where Gen AI should be implemented, it’s worth identifying inefficiencies, bottlenecks, or areas of low productivity. By automating or streamlining inefficient tasks, you can free up some time and budget, which can then be redirected to higher-value activities. Either way, generative AI can help you achieve substantial improvements in your organization’s operations.
Potential use cases of generative AI
Now, let’s dig a bit deeper and see how exactly the processes that you’ve listed can be streamlined.
#7 Which of your team’s tasks are repeatable?
When identifying the right area to implement generative AI, you may also consider these processes and tasks that are repeatable. Even if you don’t see them underperforming or, on the contrary, delivering the most value, they may be relatively easy to optimize. As such, they may be a good area to run the test implementation and see what it actually takes to start using Gen AI and avoid rookie mistakes when implementing it in a more important area.
#8 Which of your team’s tasks take the most time to finish?
When choosing the area to implement generative AI, it’s worth considering it from various angles and finding the golden mean between profitability and ease of implementation. Think about the tasks that take the most time to be finished. How much of your team’s time do these tasks take to be finished? How else could your team utilize that time? Even if certain processes are not underperforming, there may still be room for optimization.
#9 What would you like to use generative AI for?
Since you already understand which tasks take the most time to finish or have the potential to be scaled, try to determine which of them are the best candidates to be powered by Gen AI. Would your organization benefit more from using Gen AI for analyzing and understanding unstructured data, such as text, images, or videos, turning these insights into business forecasts, or maybe from content creation?
At this point, you can list all the potential use cases that come to your mind. Later on, you’ll prioritize them according to the strategic goals, ease of implementation, and estimated impact.
Read also: Is Your Data Safe With Generative AI?
Generative AI in data analysis
First, let’s take a look at the questions you should ask when implementing generative AI for analyzing and understanding unstructured data.
#10 What data does your team need to analyze?
Understanding the specific type and nature of the data enables you to identify the appropriate models and techniques to employ. Different data types may require different preprocessing steps or specialized models, and keeping that in mind helps you ensure that the generative AI system is tailored to effectively handle and interpret the specific dataset at hand. Furthermore, understanding the data requirements allows you to plan for data collection, storage, and management strategies that are aligned with the goals of your analysis.
#11 What formats of data does your team need to analyze?
The format of data that needs to be analyzed directly impacts the choice of the language model (LLM) to be utilized, as different LLMs have varying capabilities and compatibility with specific data formats. Also, understanding the data formats aids in designing appropriate preprocessing and data conversion pipelines to transform the data into a format suitable for the chosen LLM.
#12 How will you connect the models to the data sources?
Connecting the models to the data sources involves establishing robust and efficient pipelines for data ingestion and retrieval. Hence, it’s essential that you determine the best approach for accessing and fetching the required data from various sources, such as databases, APIs, or file systems. Remember to consider factors such as data streaming, real-time updates, and data security.
A well-designed connection between the models and the data sources ensures a continuous flow of data, enabling the generative AI system to stay up-to-date and provide timely insights. It also facilitates the scalability and adaptability of the system, allowing for future expansions or modifications to the data sources as needed.
Generative AI in content creation
Now, let’s see what you should consider when implementing Gen AI for content creation.
#13 In what use cases do you generate content?
Understanding the use cases will help you tailor the content generation process to meet the desired goals and requirements. Focus on the areas that you’ve already listed: the processes that deliver the most value to your organization, the ones that should be amplified, or the ones that are underperforming and need a boost to become more efficient. Ultimately, understanding the use cases allows you to prioritize and optimize the content generation process to maximize its impact on your organization’s objectives.
#14 What kind of content are you generating?
Different content types, such as text, images, audio, or a combination of these, require different models with specific capabilities. By determining what kind of content you need, you can narrow down the available models and technologies that are well-suited for generating that specific content type. For instance, if the content is predominantly textual, you’ll need to use language models such as GPT, PaLM, or their open-source alternatives. If it involves visual elements, on the other hand, generative models like Midjourney, StableDiffusion, or Dall-E might be more suitable. This question helps ensure that the chosen models align with the desired content generation objectives.
#15 How much content are you generating?
Generating content at a large scale may require more powerful computational resources and efficient parallel processing techniques. It is essential to consider factors like time constraints, frequency of content generation, and the expected volume of output. Estimating how much content the model should generate will help you determine the scale and efficiency of the generative AI system, ensure that it can handle the workload effectively, and deliver the desired quantity of content within the specified timeframe.
#16 What information do you use to generate content?
It is particularly important to consider data sources and the assessment of their reliability as it’s crucial for the credibility of the generated output. Depending on the use case, the information sources can vary, ranging from structured data sources, such as databases or APIs, to unstructured data sources, such as text corpora or online content. By considering this, you can evaluate the quality and credibility of the data sources, which directly impacts the quality and trustworthiness of the generated content. It helps in implementing mechanisms to validate and verify the information used for content generation, reducing the risk of propagating misinformation or generating biased content.
Learn from the hands-on experience of Caju AI!
The business impact of generative AI
Now that you have listed the potential use cases, let’s get back to your strategic goals and align these two.
#17 Why do you want to use generative AI?
The question of the reasons and motivations standing behind the decision about Gen AI adoption prompts a critical assessment of whether it really is the most suitable technology to address the identified needs and challenges. It may help you avoid unnecessary investment and explore alternative solutions that may be better suited to the specific requirements of your organization. Think of it as a thoughtful evaluation of whether generative AI aligns with your business goals and whether the potential benefits outweigh the associated costs and risks.
#18 How will generative AI impact your daily work?
Understanding the specific changes that Gen AI will bring to workflows, processes, and roles enables you to identify areas where generative AI can streamline tasks, augment decision-making, or enhance efficiency but also anticipate challenges it may bring. By assessing the impact on daily work, you can design effective training programs, change management strategies, and identify potential synergies between human expertise and generative capabilities.
#19 If generative AI reduces the amount of work, what will the people allocate saved time to?
By considering this question, you can proactively plan how to utilize the saved time effectively. Rather than focusing on layoffs, which may have negative implications for employee morale and retention, you may explore opportunities for upskilling or reskilling employees. This allows them to take on higher-value tasks that require human judgment, creativity, and complex problem-solving, ultimately driving innovation and adding strategic value to the organization.
#20 What will be the role and goals of generative AI?
Understanding the role and goals of Gen AI is essential for setting clear expectations and defining its purpose within the business context. By asking this question, you can identify the specific functions and responsibilities that generative AI will undertake, establish performance metrics, evaluate success criteria, and align the development and implementation efforts with the broader business objectives. It also guides ongoing monitoring and refinement of the generative AI system to ensure it continues to deliver the intended value and remains aligned with the organization’s strategic direction.
Successful path to generative AI adoption – key takeaways
Getting from the idea of generative AI implementation to full project rollout is a complex project that may take a long time and effort. Because of that, it requires a strategic approach and proper preparation.
With this first part of the list of questions to consider when implementing generative AI, you are well-prepared to identify and evaluate the right use cases for such a project. In the next part, we will look into details and focus on data management & security, compliance, technical limitations of the models, and the actual process of implementation.
Data, compliance, and a project roadmap. All that to help you plan a successful generative AI adoption.